Evaluating Performance of Different Machine Learning Algorithms for the Acute EMG Hand Gesture Datasets - Sharma, Jeevanshi; Maheshwari, Rajat; Khan, Salman; Khan, Abid Ali (Journal of Electronics and Informatics, 2022)
In this paper, different machine learning and tabular learning classification algorithms have been studied and compared on the acute hand-gesture Electromyogram dataset. The comparative study between different models such as KNN, RandomForest, TabNet, etc. depicts that small datasets can achieve high-level accuracy along with the intuition of high-performing neural net architectures through tabular learning approaches like TabNet. The performed analysis produced an accuracy of 99.9% through TabNet while other conventional classifiers also gave satisfactory results with KNN being at highest achieving accuracy of 97.8 %.
Keywords: Machine Learning; TabNet; Hand Gestures; EMG Dataset; XG Boost Algorithm